Video-Based Remote Physiological Measurement via Cross-Verified Feature Disentangling

Remote physiological measurements, e.g., remote photoplethysmography (rPPG) based heart rate (HR), heart rate variability (HRV) and respiration frequency (RF) measuring, are playing more and more important roles under the application scenarios where contact measurement is inconvenient or impossible. Since the amplitude of the physiological signals is very small, they can be easily affected by head movements, lighting conditions, and sensor diversities. To address these challenges, we propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations, and then use the distilled physiological features for robust multi-task physiological measurements. We first transform the input face videos into a multi-scale spatial-temporal map (MSTmap), which can suppress the irrelevant background and noise features while retaining most of the temporal characteristics of the periodic physiological signals. Then we take pairwise MSTmaps as inputs to an autoencoder architecture with two encoders (one for physiological signals and the other for non-physiological information) and use a cross-verified scheme to obtain physiological features disentangled with the non-physiological features. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and rPPG signals. Comprehensive experiments on different large-scale public datasets of multiple physiological measurement tasks as well as the cross-database testing demonstrate the robustness of our approach.

Authors:
Niu Xuesong, Yu Zitong, Han Hu, Li Xiaobai, Shan Shiguang, Zhao Guoying

Publication type:
A4 Article in conference proceedings

Place of publication:
Computer Vision – ECCV 2020

Keywords:
6G Publication

Published:

Full citation:
Niu X., Yu Z., Han H., Li X., Shan S., Zhao G. (2020) Video-Based Remote Physiological Measurement via Cross-Verified Feature Disentangling. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_18

DOI:
https://doi.org/10.1007/978-3-030-58536-5_18

Read the publication here:
http://urn.fi/urn:nbn:fi-fe202102195382